As I understand it, The Patriots have traded Sony Michel to the Rams for a fifth round choice and a sixth round choice. So is it a good trade? A bad trade? What does AV have to say? Well, Sony’s draft position is worth 21 AV, by the old 2012 Pro Football Reference charts. He gave them 16 AV of play over the past three years. To break even, all the Pats have to do is recover 5 AV. My tools and guessing the 5th as a 150 and the 6th as a 180 nets the Pats 13 AV. So the trade is a net positive of 8 AV. Given errors on a per-position basis, this could be break even or a small win for the Pats.

Quick note:

There is a nice video on Youtube about Brandon Staley’s 2020 Rams defense and some of the moving parts involved. Hat tip to @AlexRollinsNFL and @falcfans for this nugget. Interestingly, a move of a similar kind, to combat RPOs, has been reported in coverage of Pete Carroll’s defensive fronts.

One of the things I noticed when rereading Chris Brown’s article on Grantland, “Ode to the War Daddies” are how much the same those hybrid fronts are and the Seattle fronts are. In short, they are the same, so Bill Belichick was playing those “Seattle” fronts back in 2012.

There are a couple writers for the Dallas Morning News Dallas fans need to take note of. Michael Gehiken, Twitter handle @GehlkenNFL, is a good Dallas news guy. The other, John Owning, Twitter handle @JohnOwning is a good draft analysis guy and has some of the best articles on Dan Quinn targeted at “intelligent fans”. This link is a discussion of DQ’s pass defense philosophy, and this older draft video has it online.

If you have not seen any video of the new Atlanta Falcons DC, Dean Pees, try to give yourself 20 minutes and listen to him. I enjoyed him a lot.

Dallas DC Dan Quinn spoke recently, and said that the base defense of the Cowboys was going to be more like a 3-4. The actual quote is this:

As far as in the base packages go, it will look more like a 3-4 look, and that would have been consistent whether it was the team last year or my times with Atlanta as well. But more often than not, with most teams, the nickel packages, which teams play, I’d say, close to 60% or 70% of the time are more out of a four-down.

https://twitter.com/therealmarklane?lang=en

Some people have taken this to mean he’s going to a 3-4, and that could not be further from the truth. An examination of the coaching tree of Monte Kiffin should make that pretty clear, and we’ll provide some evidence that in fact the base defense will remain the same as the Marinelli years, even if it’s going to be tweaked a bit.

In the late 1970s Pete Carroll was a defensive assistant at Arkansas, where the head coach was Lou Holtz and his DC was Monte Kiffin. Kiffin was and is a proponent of a 4-3 under defense that he felt could stop the run and also rush the passer. In a coaching clinic recorded on Jerry Campbell Football, the effect Kiffin had on Carroll is marked.

After all the years I’ve been in football I’ve never coached anything but the 4-3 under defense. So I know this defense inside and out. I know the good side of the defense and I know the problems and weaknesses of this defense. I run it with one gap principles but can also make it work with some two gap principles.

https://jcfb.forums.net/thread/14894/the-4-3-under

So Dan Quinn is on the Kiffin tree as so. Kiffin -> Carroll -> Quinn. Marinelli is on the same tree, serving as a line coach with the Buccaneers when Kiffin was their defensive coordinator. More recently, Marinelli has been a line coach and defensive coordinator with various assignments with the Cowboys, and Marinelli’s 4-3 is what they are most familiar with.

So, some simple conclusions. The base of the C owboys base is the 4-3 under that the Cowboys have played for years, and the base is likely to be a version of the 4-3 under tweaked to have some two gapping added to the front.

But is it? Dan Quinn is known for tweaking his fronts to put his best players on the field, and he will find his best 7 over time. There are two fieldgulls.com articles (here and here) which show that he tries things in order to get best fits. The latter article talks about the successful adaptation of Red Bryant to becoming a two gap defensive end, but also that DQ that year was going to let Red Bryant 1 gap some.

Bryant said he’ll return to being more of a penetrating, one-gap defensive end and playing mostly over the right tackle.

https://www.fieldgulls.com/football-breakdowns/2013/5/31/4382318/the-seahawks-and-the-4-3-under-front-winds-of-change

Dan Quinn doesn’t let fronts get etched in stone. He crafts them.

There is a great article in Sports Illustrated that gives us some language to use for the component parts of a Seattle Hybrid defense. If the front from the defenses POV and from left to right is 4i-1-3-9, then the position names are BIG END, NOSE, 3T and LEO. In the hybrid, the BIG END is a two gap player, but because it shares so many component parts with the 4-3 under one gap, it does not have to be. Letting a BIG END 1 gap is pretty simple. The Mike linebacker just has to cover the strong side B gap. Let’s let Pete describe the left over gap assigments in the 4-3 under one gap.

The front five players I mentioned are playing aggressive defense with their outside arms free. The only thing we can’t allow to happen is for them to get hooked or reached by the defender. This alignment leaves open the strong side B Gap and the weak side A gap which are played by the Mike and Will linebackers.

https://jcfb.forums.net/thread/14894/the-4-3-under

A lot of this exercise is to eliminate any nonsense that suggests Dallas is going to a base 3-4 and DeMarcus Lawrence will have to be an OLB. He doesn’t have to be anything but DeMarcus, and he has a proven ability to defend the run as a one gapping defensive end. For that matter he might be able to two gap as well, though the addition in free agency of guys like Urban and drafting men like Osa Odighizuwa suggest perhaps a rotation at BIG END.

More importantly to me, most of the new defense should seem familiar to the veteran players. Going from a 4-3 under one gap to a hybrid isn’t a huge shift. DQ will have an off season to install, which the unfortunate Mike Nolan did not. The biggest shift is in the players the new regime likes, what physical traits they emphasize.

So now there are two teams. I will not use my usual formula for this game, as it is not valid in a season without a home field advantage. I will note that Tampa Bay has a higher Simple Ranking than Kansas City, and a larger Pythagorean. SRS would give Tampa Bay an advantage of between 2 and 3 points. That said, as a practical man, I will note that Kansas City is outperforming its expectations and has through most of the year. And Patrick Mahomes, along with Aaron Rodgers, are the two most dangerous quarterbacks in the league.

Ok, I have not been posting my playoff formulas, and for good reason, as there is no home field advantage this time around. That and the insane politics of the US transition have made it easy to focus on other things.

Looking purely at offensive stats (not the best predictors) from this source Tampa Bay should be slightly favored over Green Bay, so long as the weather is decent. Bad weather would give Green Bay an edge. The same is true for Buffalo; it should be slightly favored just looking at simple rankings or Pythagoreans. In the case of Tampa Bay, the delta is less than 2 points. For Buffalo, less than a point.

The other side of the coin is that both Green Bay and Kansas City outperformed their Pythagoreans, GB by a bit, KC by a lot. Though in KC’s case, Patrick Mahomes has to pass a concussion protocol before he would be allowed to play.

We’ll see. Stats suggest close games in any case. In terms of a good analytics take on the games, try this article from fivethirtyeight.com.

This is the last week, analyzed with my tools. I’m pondering what to do with regard to playoff predictions, as mine explicitly have home field advantage as a large component of the system, and there is no HFA this regular season. That notion will be in play the next couple days.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
256       127     49.6      30.23        19.16     11.07

Calculated Pythagorean Exponent:  3.46


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     KC       5.0    16  14   2   0  87.5  71.6   6.80   6.94 -0.14
2     GB      11.5    16  13   3   0  81.2  75.3   7.65   8.75 -1.10
3     BUF      9.0    16  13   3   0  81.2  73.2   7.71   7.88 -0.16
4     NO       5.5    16  12   4   0  75.0  77.6   9.58   9.06  0.51
5     SEA      5.5    16  12   4   0  75.0  67.6   5.46   5.50 -0.04
6     PIT      5.0    16  12   4   0  75.0  73.0   4.65   6.50 -1.85
7     BAL     14.0    16  11   5   0  68.8  81.9   8.27  10.31 -2.04
8     TB       9.5    16  11   5   0  68.8  75.6   9.38   8.56  0.82
9     IND      6.5    16  11   5   0  68.8  68.2   2.80   5.56 -2.76
10    TEN      3.0    16  11   5   0  68.8  59.6   1.59   3.25 -1.66
11    CLE      3.0    16  11   5   0  68.8  47.7  -2.58  -0.69 -1.89
12    MIA      5.5    16  10   6   0  62.5  65.0   2.97   4.12 -1.16
13    LA       5.0    16  10   6   0  62.5  68.8   5.42   4.75  0.67
14    LV       0.0    16   8   8   0  50.0  41.7  -1.95  -2.75  0.80
15    ARI     -0.5    16   8   8   0  50.0  59.5   2.57   2.69 -0.12
16    CHI     -1.0    16   8   8   0  50.0  50.5   0.24   0.12  0.11
17    MIN     -1.0    16   7   9   0  43.8  41.5  -2.41  -2.81  0.40
18    LAC     -2.0    16   7   9   0  43.8  41.1  -2.92  -2.62 -0.29
19    WAS     -2.0    16   7   9   0  43.8  51.6  -0.85   0.38 -1.22
20    NE      -4.0    16   7   9   0  43.8  43.2  -0.98  -1.69  0.71
21    NYG     -2.5    16   6  10   0  37.5  30.1  -4.44  -4.81  0.38
22    DAL     -4.5    16   6  10   0  37.5  34.9  -5.12  -4.88 -0.24
23    SF      -4.5    16   6  10   0  37.5  46.8   0.84  -0.88  1.72
24    CAR     -3.5    16   5  11   0  31.2  38.2  -1.07  -3.25  2.18
25    DEN     -4.0    16   5  11   0  31.2  24.6  -6.01  -7.69  1.68
26    DET     -6.5    16   5  11   0  31.2  24.8  -7.72  -8.88  1.15
27    CIN     -3.5    16   4  11   1  28.1  25.5  -7.49  -7.06 -0.43
28    PHI     -6.0    16   4  11   1  28.1  31.5  -4.40  -5.25  0.85
29    ATL     -3.5    16   4  12   0  25.0  46.2   0.74  -1.12  1.86
30    HOU     -6.0    16   4  12   0  25.0  34.2  -5.51  -5.00 -0.51
31    NYJ    -12.0    16   2  14   0  12.5  10.1 -11.51 -13.38  1.87
32    JAX    -12.0    16   1  15   0   6.2  16.2 -11.72 -11.62 -0.10

Kansas City has secured the #1 seed in the AFC. Green Bay has the inside track to #1 in the NFC. Home Field advantage? Will it exist in the pandemic world? There has been no HFA during the regular season, but HFA during the playoffs is a larger effect than regular season. Teams stronger than their record include Tampa Bay, New Orleans, and Baltimore.

The Jets have won again, so the Jaguars now lead for the #1 draft pick. The NFC East is actually a race again, though with the Washington run game, as long as they have a working QB, they are the likely winner there.

There were some good games this week. Colts-Steelers was a notably good game, but the best of the year, so far, was Miami-Las Vegas.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
240       120     50.0      30.08        19.12     10.96

Calculated Pythagorean Exponent:  3.38


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     KC       6.0    15  14   1   0  93.3  75.5   8.39   8.53 -0.14
2     GB       9.0    15  12   3   0  80.0  73.0   6.92   8.07 -1.14
3     BUF      8.0    15  12   3   0  80.0  69.4   6.17   6.40 -0.23
4     PIT      5.0    15  12   3   0  80.0  74.2   5.13   7.07 -1.94
5     SEA      6.0    15  11   4   0  73.3  67.7   5.61   5.67 -0.05
6     NO       5.0    15  11   4   0  73.3  73.9   8.61   7.93  0.67
7     BAL     14.0    15  10   5   0  66.7  77.1   7.15   8.67 -1.52
8     MIA      8.0    15  10   5   0  66.7  72.9   4.61   6.40 -1.79
9     TB       7.0    15  10   5   0  66.7  74.1   8.82   8.00  0.82
10    IND      6.0    15  10   5   0  66.7  65.9   2.86   5.00 -2.14
11    TEN      3.0    15  10   5   0  66.7  59.6   1.74   3.27 -1.52
12    CLE      3.0    15  10   5   0  66.7  47.2  -3.13  -0.87 -2.26
13    LA       3.0    15   9   6   0  60.0  66.5   4.92   4.33  0.58
14    ARI      2.0    15   8   7   0  53.3  61.9   3.12   3.60 -0.48
15    CHI      1.0    15   8   7   0  53.3  55.1   0.92   1.40 -0.48
16    LV      -1.0    15   7   8   0  46.7  41.1  -1.79  -3.00  1.21
17    MIN     -1.0    15   6   9   0  40.0  40.6  -2.18  -3.13  0.95
18    LAC     -3.0    15   6   9   0  40.0  37.0  -4.52  -3.93 -0.58
19    WAS     -3.0    15   6   9   0  40.0  50.0  -0.95   0.00 -0.95
20    DAL     -5.0    15   6   9   0  40.0  35.3  -4.87  -4.93  0.07
21    SF      -5.0    15   6   9   0  40.0  47.4   0.64  -0.73  1.37
22    NE      -5.0    15   6   9   0  40.0  39.3  -1.27  -2.73  1.46
23    CAR     -3.0    15   5  10   0  33.3  43.9  -0.09  -1.73  1.65
24    NYG     -3.0    15   5  10   0  33.3  28.4  -4.64  -5.40  0.76
25    DEN     -5.0    15   5  10   0  33.3  23.5  -6.16  -8.13  1.98
26    DET     -7.0    15   5  10   0  33.3  23.9  -7.90  -9.33  1.43
27    CIN     -3.0    15   4  10   1  30.0  31.8  -6.29  -5.20 -1.09
28    PHI     -6.0    15   4  10   1  30.0  32.4  -4.35  -5.20  0.85
29    ATL     -3.0    15   4  11   0  26.7  49.8   1.29  -0.07  1.35
30    HOU     -6.0    15   4  11   0  26.7  33.6  -5.69  -5.13 -0.55
31    NYJ    -10.0    15   2  13   0  13.3  10.7 -11.32 -13.33  2.02
32    JAX    -10.0    15   1  14   0   6.7  17.3 -11.75 -11.47 -0.29

Jets win! Jets win! Jets win!

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
224       111     49.6      30.03        19.28     10.75

Calculated Pythagorean Exponent:  3.47


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     KC       6.0    14  13   1   0  92.9  76.4   8.15   8.93 -0.78
2     GB       8.5    14  11   3   0  78.6  70.2   5.54   6.79 -1.24
3     BUF      7.5    14  11   3   0  78.6  65.1   4.53   4.79 -0.26
4     PIT      5.0    14  11   3   0  78.6  75.7   5.57   7.29 -1.71
5     IND      6.5    14  10   4   0  71.4  68.3   3.59   5.64 -2.05
6     SEA      5.5    14  10   4   0  71.4  66.5   4.88   5.29 -0.40
7     TEN      4.5    14  10   4   0  71.4  65.8   3.82   5.36 -1.54
8     NO       4.0    14  10   4   0  71.4  73.3   7.31   7.14  0.16
9     CLE      4.0    14  10   4   0  71.4  48.6  -1.44  -0.43 -1.01
10    BAL      9.5    14   9   5   0  64.3  76.5   7.87   8.29 -0.42
11    MIA      9.0    14   9   5   0  64.3  74.9   4.87   6.79 -1.91
12    TB       5.5    14   9   5   0  64.3  68.4   6.50   5.71  0.79
13    LA       5.0    14   9   5   0  64.3  70.4   5.30   5.43 -0.13
14    ARI      2.5    14   8   6   0  57.1  64.6   3.76   4.43 -0.66
15    LV       0.0    14   7   7   0  50.0  40.5  -2.70  -3.14  0.45
16    CHI     -1.0    14   7   7   0  50.0  49.2   0.22  -0.21  0.43
17    MIN     -1.0    14   6   8   0  42.9  43.5  -1.83  -2.00  0.17
18    WAS     -2.0    14   6   8   0  42.9  52.0  -0.22   0.50 -0.72
19    NE      -4.0    14   6   8   0  42.9  46.5   0.03  -0.86  0.89
20    NYG     -2.5    14   5   9   0  35.7  30.1  -4.33  -4.79  0.45
21    LAC     -3.0    14   5   9   0  35.7  35.4  -5.17  -4.43 -0.74
22    DEN     -5.5    14   5   9   0  35.7  22.3  -6.54  -8.50  1.96
23    DAL     -6.0    14   5   9   0  35.7  29.9  -5.97  -6.71  0.74
24    DET     -6.5    14   5   9   0  35.7  28.7  -6.27  -7.14  0.88
25    SF      -6.5    14   5   9   0  35.7  45.2  -0.25  -1.36  1.11
26    PHI     -5.5    14   4   9   1  32.1  35.2  -2.88  -4.14  1.26
27    ATL     -3.5    14   4  10   0  28.6  50.5   0.20   0.14  0.06
28    CAR     -3.5    14   4  10   0  28.6  41.6  -1.22  -2.36  1.13
29    HOU     -6.5    14   4  10   0  28.6  33.0  -4.47  -5.07  0.61
30    CIN     -3.5    14   3  10   1  25.0  28.1  -6.02  -6.00 -0.02
31    JAX     -9.0    14   1  13   0   7.1  18.3 -10.18 -10.57  0.39
32    NYJ    -13.5    14   1  13   0   7.1   8.2 -12.65 -14.79  2.13

Saw a bit of Pittsburgh – Buffalo, a game that was a lot of fun to watch. Best teams pretty much the same as last week.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
208       103     49.5      29.95        19.16     10.79

Calculated Pythagorean Exponent:  3.46


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     KC       6.0    13  12   1   0  92.3  77.7   7.91   9.38 -1.48
2     PIT      5.0    13  11   2   0  84.6  79.2   6.77   8.62 -1.84
3     GB       9.0    13  10   3   0  76.9  69.5   5.89   6.69 -0.81
4     BUF      7.0    13  10   3   0  76.9  59.6   3.05   2.92  0.13
5     NO       5.0    13  10   3   0  76.9  75.7   7.93   7.92  0.01
6     LA       7.0    13   9   4   0  69.2  72.4   7.06   6.08  0.99
7     SEA      6.0    13   9   4   0  69.2  66.1   4.91   5.31 -0.40
8     IND      6.0    13   9   4   0  69.2  67.8   3.22   5.54 -2.32
9     TEN      3.0    13   9   4   0  69.2  62.6   2.74   4.15 -1.41
10    CLE      3.0    13   9   4   0  69.2  45.2  -2.79  -1.54 -1.25
11    MIA      8.0    13   8   5   0  61.5  73.7   4.25   6.54 -2.29
12    TB       7.0    13   8   5   0  61.5  68.9   7.18   5.85  1.34
13    BAL      5.0    13   8   5   0  61.5  72.8   6.91   6.92 -0.01
14    LV       3.0    13   7   6   0  53.8  40.5  -2.47  -3.15  0.68
15    ARI      2.0    13   7   6   0  53.8  64.0   3.82   4.23 -0.41
16    MIN     -1.0    13   6   7   0  46.2  44.5  -1.45  -1.69  0.24
17    WAS     -1.0    13   6   7   0  46.2  53.7  -0.13   0.92 -1.05
18    CHI     -3.0    13   6   7   0  46.2  47.3   0.37  -0.69  1.07
19    NE      -3.0    13   6   7   0  46.2  49.4   0.34  -0.15  0.50
20    NYG     -2.0    13   5   8   0  38.5  33.3  -3.27  -4.08  0.80
21    SF      -5.0    13   5   8   0  38.5  46.9   0.98  -0.85  1.83
22    DEN     -5.0    13   5   8   0  38.5  26.1  -5.24  -6.92  1.69
23    DET     -6.0    13   5   8   0  38.5  31.3  -5.23  -6.08  0.85
24    PHI     -5.0    13   4   8   1  34.6  35.8  -2.69  -3.92  1.23
25    ATL     -3.0    13   4   9   0  30.8  51.6   0.34   0.46 -0.13
26    CAR     -3.0    13   4   9   0  30.8  43.3  -0.78  -1.92  1.15
27    LAC     -3.0    13   4   9   0  30.8  33.5  -5.76  -5.00 -0.76
28    HOU     -6.0    13   4   9   0  30.8  33.6  -4.55  -4.92  0.38
29    DAL     -7.0    13   4   9   0  30.8  26.5  -6.98  -7.85  0.87
30    CIN     -4.0    13   2  10   1  19.2  24.5  -8.03  -7.23 -0.80
31    JAX     -8.0    13   1  12   0   7.7  21.0  -9.90  -9.38 -0.52
32    NYJ    -17.0    13   0  13   0   0.0   6.6 -14.44 -16.15  1.72